To better sustain passengers' loyalty towards bus service, this paper addressed the modeling of the public transit loyalty by the use of structural equation model. As a novel hypothesis, the emotional value was considered to have effects on the perceived value of bus services in this study, which reflected the degree of passengers' emotional dependence on the public transit. Specifically, in order to better assess the loyalty, seven unobserved variables were measured to construct the structural model, namely, "service guarantee," "operational services and efficiency," "emotional value," "perceived value," "expectation," "satisfaction," and "loyalty." The goodness-of-fit of the model was estimated and evaluated by using the survey data harvested from Xiamen, China. Besides, the index score of variables was also computed to help determine targeted approaches to better improve the level of bus service. The results indicated that the time cost and the monetary cost actually had no effects on the perceived value of users in the case study. At the same time, however, it also proved that passengers' emotional value towards the public transit indeed affected passengers' perception of the service value. In addition, whether users' perceived value was as expected determined how much passengers satisfied with the service. Regarding the index score of variables, it indicated a great dissatisfaction of passengers towards the current bus service. Unexpectedly, the score of loyalty even still retained a relatively high level, which reflected continue-to-use willingness of passengers. It implied that being subject to economic conditions and other factors, passengers were captive and had to continue relying on the public transit, in spite of their dissatisfaction. As for the improvement direction of bus services, targeted approaches should be determined to improve the quality of bus service, regarding the aspects of "condition of facilities in the bus," "driving stability and comfort," "vehicle speed," and "safety."
Accidents, bad weathers, traffic congestions, etc. led to the uncertainties of travel times in real-life road networks, which greatly affected the quality of individual’s life and the reliability of transportation system. This paper addressed the school bus routing problem in such a stochastic and time-dependent road environment. Firstly, the problem was set based on a single-school configuration, and the students were picked up at their homes, which was in line with the current situation of school bus systems in China. Thus, it could be regarded as an independent problem of school bus route generation in random dynamic networks, which could be solved as a variant of extended Vehicle Routing Problem. However, due to the fluctuation and uncertainty of link travel times, the arrival time at each stop including the destination was varying. Therefore, the selection of optimal path connecting the current service node with the next one was treated as a sub-problem in this study, where the reliability of travel times in the stochastic and time-varying network was highly concerned by such time-rigid commuters. To this end, a Robust Optimal Schedule Times model with a hard time windows constraint was built to generate a most cost-reliable route for school buses. By the use of Robust Optimization method, it was intended to minimize the worst-case total cost which combined the cost of earlier schedule delays with the disutility of travel times. It was also proved that the proposed model could be converted into solving a conventional problem in deterministic dynamic networks for a reduction of computation complexity, which provided the potential of applying to the practical problems. Finally, the validity of the proposed model and its performance evaluation was analyzed through a small-scale computational instance, where all the link travel times in the simulated network were attributed to both time-varying and stochastic. Then, a mathematical programming solver was used to find the exact optimal solution. The results indicated that the model was valid, and the necessity of considering the stochastic and time-dependent nature of transportation networks was also confirmed in the case study.
Understanding the travel patterns of public transit commuters was important to the efforts towards improving the service quality, promoting public transit use, and better planning the public transit system. Smartcard data, with its wide coverage and relative abundance, could provide new opportunities to study public transit riders’ behaviors and travel patterns with much less cost than conventional data source. However, the major limitation of smartcard data is the absence of social attributes of the cardholders, so that it cannot clearly extract public transit commuters and explain the mechanism of their travel behaviors. This study employed a machine learning approach called Naive Bayesian Classifier (NBC) to identify public transit commuters based on both the smartcard data and survey data, demonstrated in Xiamen, China. Compared with existing methods which were plagued by the validation of the accuracy of the identification results, the adopted approach was a machine learning algorithm with functions of accuracy checking. The classifier was trained and tested by survey data obtained from 532 valid questionnaires. The accuracy rate for identification of public transit commuters was 92% in the test instances. Then, under a low calculation load, it identified the objectives in smartcard data without requiring travel regularity assumptions of public transit commuters. Nearly 290,000 cardholders were classified as public transit commuters. Statistics such as average first boarding time and travel frequency of workdays during peak hours were obtained. Finally, the smartcard data were fused with bus location data to reveal the spatial distributions of the home and work locations of these public transit commuters, which could be utilized to improve public transit planning and operations.
This study adopted smart card data collected from metro systems to identify city centers and illustrate how city centers interacted with other regions. A case study of Xi’an, China, was given. Specifically, inflow and outflow patterns of metro passengers were characterized to measure the degree of population agglomeration of an area, i.e., the centricity of an area. On this basis, in order to overcome the problem of determining the boundaries of the city centers, Moran’s I was adopted to examine the spatial correlation between the inflow and outflow of ridership of adjacent areas. Three residential centers and two employee centers were identified, which demonstrated the polycentricity of urban structure of Xi’an. With the identified polycenters, the dominant spatial connections with each city center were investigated through a multiple linkage analysis method. The results indicated that there were significant connections between residential centers and employee centers. Moreover, metro passengers (commuters mostly) flowing into the identified employee centers during morning peak-hours mainly came from the northern and western area of Xi’an. This was consistent with the interpretation of current urban planning, which validated the effectiveness of the proposed methods. Policy implications were provided for the transport sector and public transport operators.
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